(04) Team research · Cambridge Science Competition · Gold
Storage Temperature and Microbial Growth in Melons
What is the most suitable storage temperature for watermelon, cantaloupe and honeydew to minimise microbial growth and reduce potential food-safety risks?
Pham Ha Khanh Chi · team project, main writer of the research paper
- Melons3varieties
- Temperatures45 to 30°C
- Agar plates363 per condition
- Observations2day 4 and week 1
- ResultGoldCambridge Science Competition
In short
We expected colder to always mean safer. It didn’t: the relationship between temperature and microbial growth was not the same for all three melons. Cantaloupe and watermelon did best at 5°C, but honeydew showed its lowest growth at 10°C.
Research overview
The project investigated the relationship between storage temperature and microbial growth in three commonly consumed melons. Motivated by the risk of food contamination and food-borne illness, we aimed to test whether commonly recommended storage temperatures were effective for these fruits rather than relying solely on general food-storage guidelines.
The investigation tested four storage temperatures (5°C, 10°C, 20°C and 30°C) and compared microbial growth across the three melon varieties. Our original hypothesis was that lower storage temperatures would consistently result in less microbial growth.
The project connected experimental microbiology with a practical public-health question: whether evidence from controlled experiments could inform safer food-storage practices.
Approach
We designed a comparative experiment to investigate the effects of storage temperature and melon variety on microbial growth.
- 01
Variables. Storage temperature and melon variety were the independent variables. Microbial colony counts on agar plates were the main measure of growth.
- 02
Controlled conditions. We attempted to standardise the melon sample mass, the volume of juice transferred to each plate (0.5 cm³), equipment, storage conditions, and handling procedures.
- 03
Data collection. Samples were prepared from melon juice, spread across agar plates, and incubated at 40°C. We photographed the plates and estimated colony counts after four days and again after one week.
- 04
Data analysis. We calculated average colony counts from three plates for each condition, compared growth across temperatures and melon varieties, and examined changes between observation periods.
- 05
Preliminary testing and refinement. An initial trial revealed that scraping melon flesh directly onto the plates produced inconsistent sample quantities and uneven growth. We changed the method to crush the melon, extract its juice, measure it with a pipette, and spread it across the agar. We also improved the photography setup to make colonies easier to identify and count.
- 06
Safety and collaboration. We used gloves, goggles, disinfection, and sealed plates, while dividing experimental tasks among team members.
Results
The results did not fully support our original hypothesis that lower temperatures would always produce less microbial growth. Instead, the observed patterns differed between melon varieties.
| Melon variety | Main finding | Conclusion in the report |
|---|---|---|
| Cantaloupe | 20°C produced the highest colony count, with a substantial increase after one week. | 5°C was identified as the most suitable tested temperature. |
| Watermelon | 20°C produced high colony counts, while growth at 10°C increased noticeably over time. Mould obscured some colonies at 30°C. | 5°C was identified as the most suitable tested temperature. |
| Honeydew | 10°C consistently had the lowest recorded colony counts, while 5°C and 30°C showed substantial growth. | 10°C was identified as the most suitable tested temperature. |
The key finding was that the relationship between temperature and microbial growth was not uniform across all three melon varieties. Although lower temperatures appeared beneficial for cantaloupe and watermelon, honeydew showed a different pattern, with the lowest recorded growth at 10°C.
The experiment also demonstrated that the observation period mattered: colony counts generally increased over time, but the magnitude of the increase varied across conditions.
These findings challenged our initial assumption and highlighted the importance of testing hypotheses empirically rather than generalising from existing recommendations.
Evaluation
Strengths
- 01
A clear, testable research question. The project translated a real-world food-safety concern into measurable experimental variables.
- 02
Comparative experimental design. Testing three melon varieties across four temperatures enabled comparisons between different storage conditions.
- 03
Replicate measurements. Three agar plates per condition provided a basis for calculating averages and observing variation between samples.
- 04
Iterative problem-solving. The preliminary trial identified problems with inconsistent sample quantities, uneven spreading, and poor image quality. We modified the method before conducting the main investigation.
- 05
Longitudinal observation. Recording results after four days and one week allowed us to examine changes over time rather than relying on a single measurement.
- 06
Critical reflection. The report acknowledged experimental errors, uncontrolled variables, and the need for further trials.
Limitations and improvements
| Limitation | Potential impact | Proposed improvement |
|---|---|---|
| Manual colony counting and blurry photographs | Colonies could be missed, counted twice, or confused with overlapping growth. | Use standardised photography, image-analysis software, and an additional measure of the proportion of agar covered by growth. |
| Small number of replicates | Three plates per condition may not adequately capture biological variation. | Repeat the experiment across more independent samples and experimental runs. |
| Uncontrolled initial contamination and storage history | Differences in the melons before testing could affect the results independently of temperature. | Standardise sample sourcing, maturity, preparation, and pre-experiment storage as far as possible. |
| Uneven distribution and uncertain sample quantities | Differences between plates could reflect the preparation method rather than the experimental variables. | Use calibrated measuring equipment and a more consistent sample-preparation protocol. |
| Limited temperature range and melon varieties | The findings cannot establish the best storage conditions for all melons or temperatures. | Test additional temperatures and varieties in follow-up experiments. |
| Microbial identity was not established | Colony counts alone cannot distinguish bacteria from fungi or establish whether the organisms are pathogenic. | Use appropriate supervised microbiological identification methods and describe the measured outcome as total observable microbial growth unless identities are verified. |
An important qualification: the findings should be treated as preliminary rather than definitive food-storage recommendations. The report’s colony-count estimates were affected by blurry growth, mould, and variation between plates. In addition, the study measured visible microbial growth on agar, not foodborne illness risk directly. More rigorous testing would be needed before drawing firm conclusions about food safety.
The project’s strongest feature was not that it established a universally optimal storage temperature, but that it exposed the limitations of a simple hypothesis and generated questions that could be investigated further.
The combination of preliminary testing, methodological adjustments, quantitative observations, and explicit evaluation demonstrated an understanding that scientific conclusions depend on the quality of the evidence.
My role
We carried out the whole project together as a team, from designing the experiment to preparing, observing and counting the plates, so there were no fixed roles. I wrote most of the research paper.
What I learnt
This project taught me that scientific investigation involves much more than developing a hypothesis and collecting data. I learnt to translate a real-world problem into a testable research question, design an experiment around measurable variables, and evaluate whether the evidence genuinely supported our initial assumptions.
One of my most important lessons came from our preliminary trial. Our original method produced inconsistent sample quantities and uneven microbial growth, making the results difficult to compare. Rather than proceeding with a flawed method, we reconsidered our procedure, standardised the volume of melon juice, and improved how we recorded the results. This showed me how experimental design and data quality directly influence the reliability of scientific conclusions.
I also learnt the importance of questioning unexpected results. We initially expected lower temperatures to consistently suppress microbial growth, but the three melon varieties exhibited different patterns. Analysing these differences taught me not to force observations to fit a hypothesis, but to recognise uncertainty, consider alternative explanations, and distinguish between what the data showed and what we could reasonably conclude.
Finally, working in a team developed my ability to divide responsibilities, communicate findings, solve practical problems, and reflect on weaknesses in our approach. Winning gold in the Cambridge Upper Secondary Science Competition was a rewarding outcome, but the experience also gave me a more valuable understanding of scientific thinking: a good investigation does not simply seek to confirm an answer; it tests assumptions, acknowledges limitations, and identifies what needs to be investigated next.